Source of the Data:

It’s taken from Open Government Data (OGD) Platform India link is below here https://data.gov.in/catalog/district-wise-crimes-committed-against-women Reference URL of Resource https://ncrb.gov.in/

Original Authors/Contributors of the Data:

Data Uploaded Date

Purpose of the Data

To know and analysis various factor of Crimes committed against Women during 2015. To analysis crime against Women of every State of India .To visualise different-different type of crimes (like Rape, Dowry Deaths, Kidnapping, Cruelty by Husband or his Relatives… etc) according to State and district. By all this To find which states are unsafe for women and which states are safe.

Data Description

The data refers to district wise details on crimes against women during the years. The nature of such crimes includes Rape, Kidnapping and Abduction, Dowry Death, Assault on women with intent to outrage her modesty, Insult to modesty of Women, Cruelty by Husband or his Relatives and Importation of Girls. This data is collected to study one of the major issues of the country namely “crime against Women”. Various analysis of the data is done for strategic planning and making policies to prevent crime against Women.

EXPLORATORY DATA ANALYSIS

Reading Working Project Data

data = read.csv("C:/Users/verma/Documents/DESKTOP2.0/project/District-wise_Crimes_committed_against_Women_2015.csv")
colnames(data)
##  [1] "State..UT"                                                
##  [2] "Sl..No."                                                  
##  [3] "District..Area"                                           
##  [4] "Year"                                                     
##  [5] "Rape"                                                     
##  [6] "Attempt.to.commit.Rape"                                   
##  [7] "Kidnapping...Abduction_Total"                             
##  [8] "Dowry.Deaths"                                             
##  [9] "Assault.on.Women.with.intent.to.outrage.her.Modesty_Total"
## [10] "Insult.to.the.Modesty.of.Women_Total"                     
## [11] "Cruelty.by.Husband.or.his.Relatives"                      
## [12] "Importation.of.Girls.from.Foreign.Country"                
## [13] "Abetment.of.Suicides.of.Women"                            
## [14] "Dowry.Prohibition.Act..1961"                              
## [15] "Indecent.Representation.of.Women..P..Act..1986"           
## [16] "Protection.of.Children.from.Sexual.Offences.Act"          
## [17] "Protection.of.Women.from.Domestic.Violence.Act..2005"     
## [18] "Immoral.Traffic.Prevention.Act"                           
## [19] "Total.Crimes.against.Women"
library(ggplot2)
library(moments)
library(gridExtra)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:gridExtra':
## 
##     combine
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout

Description of columns of chosen Data set:

Column Name Description of column Type (character, integer, numeric, logical)
State/ UT State/ UT Names Character
Sl. No. Serial no. of District in States Integer
District/ Area District/ Area of States Character
Year Only Year is 2015 Integer
Rape No. of Rape Cases Integer
Attempt to commit Rape No. of Attempt to commit Rape Integer
Kidnapping & Abduction_Total No. of Kidnapping & Abduction Cases Integer
Dowry Deaths No. Dowry Deaths Integer
Assault on Women with intent to outrage her Modesty_Total No. of Assault on Women with intent to outrage her Modesty Cases Integer
Insult to the Modesty of Women_Total No. of Insult to the Modesty of Women Cases Integer
Cruelty by Husband or his Relatives No. of Cruelty by Husband/his Relatives Cases Integer
Importation of Girls from Foreign Country Cases of Importation of Girls from Foreign Country Integer
Abetment of Suicides of Women Cases of Abetment of Suicides of Women Integer
Dowry Prohibition Act, 1961 Cases under Dowry Prohibition Act(1961) Integer
Indecent Representation of Women (P) Act, 1986 Cases under Indecent Representation of Women Act(1986) Integer
Protection of Children from Sexual Offences Act Cases under Protection of Children from Sexual Offences Act Integer
Protection of Women from Domestic Violence Act, 2005 Cases under Protection of Women from Domestic Violence Act(2005) Integer
Immoral Traffic Prevention Act Cases under Immoral Traffic Prevention Act Integer
Total Crimes against Women Total Crimes Cases of each district in states Integer

Visualize the Rape Case in States 

StatesRep = c(data$State..UT)
States = c()
for(i in 1:length(data$State..UT))
{
  if(data$District..Area[i] == "Total District(s)"){
    States = data$State..UT[data$District..Area == "Total District(s)"]
    States
  }
    
}
States
##  [1] "Andhra Pradesh"    "Arunachal Pradesh" "Assam"            
##  [4] "Bihar"             "Chhattisgarh"      "Goa"              
##  [7] "Gujarat"           "Haryana"           "Himachal Pradesh" 
## [10] "Jammu & Kashmir"   "Jharkhand"         "Karnataka"        
## [13] "Kerala"            "Madhya Pradesh"    "Maharashtra"      
## [16] "Manipur"           "Meghalaya"         "Mizoram"          
## [19] "Nagaland"          "Odisha"            "Punjab"           
## [22] "Rajasthan"         "Sikkim"            "Tamil Nadu"       
## [25] "Telangana"         "Tripura"           "Uttar Pradesh"    
## [28] "Uttarakhand"       "West Bengal"       "A & N Islands"    
## [31] "Chandigarh"        "D&N Haveli"        "Daman & Diu"      
## [34] "Delhi UT"          "Lakshadweep"       "Puducherry"
length(States)
## [1] 36
RapeCaseS = data$Rape[data$District..Area == "Total District(s)"]
my_bar = barplot(RapeCaseS, names.arg = States, las = 2, ylim = c(0,5000), cex.names = 0.6, col= rainbow(36), main = "Rape Cases in States(in number)") 

text(my_bar,RapeCaseS+150, paste( RapeCaseS, sep="") ,cex=0.6) 

plot_ly(values = RapeCaseS ,labels = States ,type = 'pie', title = "Rape Cases in States" )

Summary of Column(RapeCases)

Minimun =min(RapeCaseS)
q_1 =quantile(RapeCaseS)[2]
Median =median(RapeCaseS)
Mean =mean(RapeCaseS)
q_3 =quantile(RapeCaseS)[4]
Max =max(RapeCaseS)
Var =var(RapeCaseS)
Skewness =skewness(RapeCaseS)
Kurtosis =kurtosis(RapeCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_RapeCaseS=c(Minimun ,q_1 ,Median, Mean, q_3 ,Max ,Skewness ,Kurtosis ,Var )
#table_age
df1=data.frame(Statistic, Value =value_RapeCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum 0.000000e+00
## 2 1st Quartile 6.775000e+01
## 3       Median 4.620000e+02
## 4         Mean 9.625278e+02
## 5 3rd Quartile 1.213250e+03
## 6          Max 4.391000e+03
## 7     Varience 1.521205e+00
## 8     Skewness 4.456413e+00
## 9     Kurtosis 1.467639e+06

COMMENT:

  • Number of Rape Cases in Madhya Pradesh is High compare to other States and UT. 12.7 percentage of Rape cases happend in 2015. It is in first Unsecured States for women with 4391 Rape cases, In Second Unsecured States Maharashtra with 4144 Rape cases and third Unsecured States Rajasthan with 3644 no. of Rape cases in 2015.
  • Where Lakshadweep has 0 no of Rape cases

Removing the row of Total case in each States

TC = which(data$District..Area == "Total District(s)")
data2 = data %>%  filter(!row_number() %in% TC)

Column Attempt to commit Rape cases

Visualize the Attempt to commit Rape cases

CommitRapeCaseS = data$Attempt.to.commit.Rape[data$District..Area == "Total District(s)"]
my_bar = barplot(CommitRapeCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0, 2000))

text(my_bar,CommitRapeCaseS+100, paste( CommitRapeCaseS, sep="") ,cex=0.6) 

plot_ly(values = CommitRapeCaseS ,labels = States ,type = 'pie', title = "Attempt to Rape Cases in states" )

Some statistic of this feature

Minimun =min(CommitRapeCaseS)
q_1 =quantile(CommitRapeCaseS)[2]
Median =median(CommitRapeCaseS)
Mean =mean(CommitRapeCaseS)
q_3 =quantile(CommitRapeCaseS)[4]
Max =max(CommitRapeCaseS)
Var =var(CommitRapeCaseS)
Skewness =skewness(CommitRapeCaseS)
Kurtosis =kurtosis(CommitRapeCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_CommitRapeCaseS =c(Minimun ,q_1 ,Median ,Mean ,q_3 ,Max ,Skewness ,Kurtosis ,Var )
#table_age
df1=data.frame(Statistic, Value =  value_CommitRapeCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum     0.000000
## 2 1st Quartile     1.000000
## 3       Median    20.000000
## 4         Mean   123.250000
## 5 3rd Quartile   110.750000
## 6          Max  1551.000000
## 7     Varience     3.935056
## 8     Skewness    19.909701
## 9     Kurtosis 78736.764286

COMMENT:

  • No of Attempt to commit Rape Cases is High in West Bangal with 1551. 35 percentage of Attempt to commit Rape Cases is happend in West Bangal, 2nd highest cases in Assam(11.2% of Attempt to commit Rape Cases) and 3rd highest cases in Uttar Pradesh(9.51% of Attempt to commit Rape Cases)
  • The Lakshadweep, Goa, A & N Islands, D&N Haveli and Daman & Diu has 0 number of cases.

Column Kidnapping Cases

Visualize the Kidnapping Cases

kidnappingCaseS = data$Kidnapping...Abduction_Total[data$District..Area == "Total District(s)"]
my_bar = barplot(kidnappingCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,12000), main = "Kidnapping Cases in states(number)")
text(my_bar,kidnappingCaseS+400, paste( kidnappingCaseS, sep="") ,cex=0.5) 

plot_ly(values = kidnappingCaseS ,labels = States ,type = 'pie', title = "Kidnapping Cases in states" )

Some statistic of this feature

Minimun_temp=min(kidnappingCaseS)
q_1_temp=quantile(kidnappingCaseS)[2]
Median_temp=median(kidnappingCaseS)
Mean_temp=mean(kidnappingCaseS)
q_3_temp=quantile(kidnappingCaseS)[4]
Max_temp=max(kidnappingCaseS)
Var_temp=var(kidnappingCaseS)
Skewness_temp=skewness(kidnappingCaseS)
Kurtosis_temp=kurtosis(kidnappingCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_kidnappingCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value =  value_kidnappingCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum 0.000000e+00
## 2 1st Quartile 6.700000e+01
## 3       Median 6.660000e+02
## 4         Mean 1.646583e+03
## 5 3rd Quartile 2.398750e+03
## 6          Max 1.013500e+04
## 7     Varience 1.827504e+00
## 8     Skewness 6.517332e+00
## 9     Kurtosis 5.162887e+06

COMMENT:

  • No of Kidnapping Cases is High in Uttar Pradesh with 10135. 17.1 percentage of Kidnapping Cases is happend in Uttar Pradesh, 2nd highest cases in Bihar(8.7% of Kidnapping Cases) and 3rd highest cases in Maharashtra(8.6% of Kidnapping Cases) and
  • The Lakshadweep has 0 lowest cases,2nd lowest Mizorm has 8 cases and 3rd lowest D&N Haveli has 9 cases.

Column of Dowry Deaths Cases

Visualize the Dowry Deaths Cases

DowryDeathsCaseS = data$Dowry.Deaths[data$District..Area == "Total District(s)"]
my_bar = barplot(DowryDeathsCaseS, names.arg = States, las =2, col= rainbow(36),ylim = c(0,2500) ,cex.names = 0.6, main = "Dowry Deaths Cases in states(in number)")
text(my_bar,DowryDeathsCaseS+120, paste( DowryDeathsCaseS, sep="") ,cex=0.6)

plot_ly(values = DowryDeathsCaseS ,labels = States ,type = 'pie', title = "Dowry Deaths Cases in states"  )

Some statistic of this feature

Minimun_temp=min(DowryDeathsCaseS)
q_1_temp=quantile(DowryDeathsCaseS)[2]
Median_temp=median(DowryDeathsCaseS)
Mean_temp=mean(DowryDeathsCaseS)
q_3_temp=quantile(DowryDeathsCaseS)[4]
Max_temp=max(DowryDeathsCaseS)
Var_temp=var(DowryDeathsCaseS)
Skewness_temp=skewness(DowryDeathsCaseS)
Kurtosis_temp=kurtosis(DowryDeathsCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_DowryDeathsCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value =  value_DowryDeathsCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum      0.00000
## 2 1st Quartile      1.00000
## 3       Median     41.00000
## 4         Mean    212.05556
## 5 3rd Quartile    256.00000
## 6          Max   2335.00000
## 7     Varience      3.62952
## 8     Skewness     17.27124
## 9     Kurtosis 189874.28254

COMMENT:

* No of Dowry Death Cases is High in Uttar Pradesh with 2335. 30.6 percentage of Dowry Death Cases is happend in Uttar Pradesh, 2nd highest cases in Bihar(15.1 % of Kidnapping Cases) and  3rd highest cases in Madhya Pradesh(8.7 % of Kidnapping Cases) 
* The Lakshadweep, Arunachal Pradesh, Goa, Manipur, Mizorm, Nagaland, A & N Islands and Daman & Diu has 0 no of Dowry Death Cases.

Column of Assault on Women with intent to outrage her Modesty

Visualize the Assault on Women with intent to outrage her Modesty

AssaultCaseS = data$Assault.on.Women.with.intent.to.outrage.her.Modesty_Total[data$District..Area == "Total District(s)"]

my_bar= barplot(AssaultCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,13000), main = "Assault on Women Cases in states(number)" )
text(my_bar,AssaultCaseS+400, paste( AssaultCaseS, sep="") ,cex=0.6)

plot_ly(values = AssaultCaseS ,labels = States ,type = 'pie', title = "Assault on Women Cases in states" )

Some statistic of this feature

Minimun_temp=min(AssaultCaseS)
q_1_temp=quantile(AssaultCaseS)[2]
Median_temp=median(AssaultCaseS)
Mean_temp=mean(AssaultCaseS)
q_3_temp=quantile(AssaultCaseS)[4]
Max_temp=max(AssaultCaseS)
Var_temp=var(AssaultCaseS)
Skewness_temp=skewness(AssaultCaseS)
Kurtosis_temp=kurtosis(AssaultCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_AssaultCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value =  value_AssaultCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum 5.000000e+00
## 2 1st Quartile 8.200000e+01
## 3       Median 9.835000e+02
## 4         Mean 2.289500e+03
## 5 3rd Quartile 4.373750e+03
## 6          Max 1.171300e+04
## 7     Varience 1.369116e+00
## 8     Skewness 4.274321e+00
## 9     Kurtosis 8.690933e+06

COMMENT:

  • No of Assault on Women with intent to outrage her Modesty Cases is High in Maharashtra with 11713 Cases. 14.2 percentage of Assault Cases is happend in Maharashtra, 2nd highest cases in Madhya Pradesh(9.77 % of Assault Cases) and 3rd highest cases in Uttar Pradesh(9.57 % of Assault Cases)
  • The D$N haveli has 5 cases of Assault on Women, Lakshadweep has 2nd lowest no of cases(6) and Daman & Diu has 3rd lowest no of cases(7).

Column of Insult to the Modesty of Women Cases

Visualize the Insult to the Modesty of Women Cases

InsultCaseS = data$Insult.to.the.Modesty.of.Women_Total[data$District..Area == "Total District(s)"]

my_bar = barplot(InsultCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,2500), main = "Insult to the Modesty of Women Cases in states" )
text(my_bar, InsultCaseS+150, paste( InsultCaseS, sep="") ,cex=0.6)

plot_ly(values = InsultCaseS ,labels = States ,type = 'pie', title = "Insult to the Modesty of Women Cases in states" )

Some statistic of this feature

Minimun_temp=min(AssaultCaseS)
q_1_temp=quantile(AssaultCaseS)[2]
Median_temp=median(AssaultCaseS)
Mean_temp=mean(AssaultCaseS)
q_3_temp=quantile(AssaultCaseS)[4]
Max_temp=max(AssaultCaseS)
Var_temp=var(AssaultCaseS)
Skewness_temp=skewness(AssaultCaseS)
Kurtosis_temp=kurtosis(AssaultCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_AssaultCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_AssaultCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum 5.000000e+00
## 2 1st Quartile 8.200000e+01
## 3       Median 9.835000e+02
## 4         Mean 2.289500e+03
## 5 3rd Quartile 4.373750e+03
## 6          Max 1.171300e+04
## 7     Varience 1.369116e+00
## 8     Skewness 4.274321e+00
## 9     Kurtosis 8.690933e+06

COMMENT:

  • No of Insult to the Modesty of Women Cases is High in Andhra Pradesh with 2200 Cases. 25.3 percentage of Insult Cases is happend in Andhra Pradesh, 2nd highest cases in Delhi UT(17.2 % of Insult Cases) and 3rd highest cases in Telangana(14.8 % of Insult Cases)
  • D&N Haveli and Daman & Diu has lowest case(0).

Column of Cruelty by Husband or his Relatives Cases

Visualize of Cruelty by Husband or his Relatives Cases

CrueltyCaseS = data$Cruelty.by.Husband.or.his.Relatives[data$District..Area == "Total District(s)"]
my_bar = barplot(CrueltyCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,25000), main = "Cruelty by Relatives Cases in states(in number)" )
text(my_bar,CrueltyCaseS+600, paste( CrueltyCaseS, sep="") ,cex=0.6)

plot_ly(values = CrueltyCaseS ,labels = States ,type = 'pie', title = "Cruelty by Relatives Cases in states" )

Some statistic of this feature

Minimun_temp=min(CrueltyCaseS)
q_1_temp=quantile(CrueltyCaseS)[2]
Median_temp=median(CrueltyCaseS)
Mean_temp=mean(CrueltyCaseS)
q_3_temp=quantile(CrueltyCaseS)[4]
Max_temp=max(CrueltyCaseS)
Var_temp=var(CrueltyCaseS)
Skewness_temp=skewness(CrueltyCaseS)
Kurtosis_temp=kurtosis(CrueltyCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_CrueltyCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_CrueltyCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum 2.000000e+00
## 2 1st Quartile 3.400000e+01
## 3       Median 1.101500e+03
## 4         Mean 3.150083e+03
## 5 3rd Quartile 3.877250e+03
## 6          Max 2.016300e+04
## 7     Varience 2.027406e+00
## 8     Skewness 7.150055e+00
## 9     Kurtosis 2.099172e+07

COMMENT:

  • No of Cruelty by Husband or his Relatives Cases is High in West Bengal with 20163 Cases. 17.8 percentage of Cruelty Cases is happend in Maharashtra, 2nd highest cases in Rajasthan(12.7 % of Cruelty Cases) and 3rd highest cases in Assam(9.9 % of cruelty Cases)
  • Sikkim, D&N Haveli and Lakshdweep Has lowest Cruelty cases, that is 0.

Column of Importation of Girls from Foreign Country Cases

Visualize the Importation of Girls from Foreign Country Cases

ImportationCaseS = data$Importation.of.Girls.from.Foreign.Country[data$District..Area == "Total District(s)"]
my_bar = barplot(ImportationCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,6), main = "Importation of Girls from Foreign Country Cases in states" )
text(my_bar,ImportationCaseS+0.2, paste( ImportationCaseS, sep="") ,cex=0.6)

plot_ly(values = ImportationCaseS ,labels = States ,type = 'pie', title = "Importation of Girls from Foreign Country Cases in states" )

Some statistic of this feature

Minimun_temp=min(ImportationCaseS)
q_1_temp=quantile(ImportationCaseS)[2]
Median_temp=median(ImportationCaseS)
Mean_temp=mean(ImportationCaseS)
q_3_temp=quantile(ImportationCaseS)[4]
Max_temp=max(ImportationCaseS)
Var_temp=var(ImportationCaseS)
Skewness_temp=skewness(ImportationCaseS)
Kurtosis_temp=kurtosis(ImportationCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_ImportationCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_ImportationCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic      Value
## 1      Minimum  0.0000000
## 2 1st Quartile  0.0000000
## 3       Median  0.0000000
## 4         Mean  0.1666667
## 5 3rd Quartile  0.0000000
## 6          Max  4.0000000
## 7     Varience  4.5158676
## 8     Skewness 22.6620499
## 9     Kurtosis  0.5428571

COMMENT:

  • Their is total 6 cases of Importation of girls from Foreign Country Cases.
  • Uttarakhand has 2 cases and West Bengal has 4 cases.

Column of Abetment of Suicides of Women Cases

Visualize for Abetment of Suicides of Women Cases

suicidesCaseS = data$Abetment.of.Suicides.of.Women[data$District..Area == "Total District(s)"]
my_bar = barplot(suicidesCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,800), main = "Abetment of Suicide of Women Cases in states" )
text(my_bar,suicidesCaseS+40, paste( suicidesCaseS, sep="") ,cex=0.6)

plot_ly(values = suicidesCaseS ,labels = States ,type = 'pie', title = "Abetment of Suicide of Women Cases in states" )

Some statistic of this feature

Minimun_temp=min(suicidesCaseS)
q_1_temp=quantile(suicidesCaseS)[2]
Median_temp=median(suicidesCaseS)
Mean_temp=mean(suicidesCaseS)
q_3_temp=quantile(suicidesCaseS)[4]
Max_temp=max(suicidesCaseS)
Var_temp=var(suicidesCaseS)
Skewness_temp=skewness(suicidesCaseS)
Kurtosis_temp=kurtosis(suicidesCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_suicidesCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_suicidesCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum     0.000000
## 2 1st Quartile     0.000000
## 3       Median     5.000000
## 4         Mean   112.777778
## 5 3rd Quartile   146.000000
## 6          Max   702.000000
## 7     Varience     1.896123
## 8     Skewness     5.719625
## 9     Kurtosis 34285.777778

COMMENT:

  • No of Abetment Of Suicide Cases is High in Maharashtra with 702 Cases. 17.3 percentage of Suicide Cases is happend in Maharashtra, 2nd highest cases in Telangana(14.5 % of Suicide Cases) and 3rd highest cases in Madhya Pradesh(14.2 % of suicide Cases)
  • Assam, Bihar, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, D&N Haveli, Daman & Diu, Lakshadweep and Puducherry has 0 no of cases of Suicide.

Column of Cases under Dowry Prohibition Act (1961)

Visualize the Cases under Dowry Prohibition Act (1961)

DowryCaseS = data$Dowry.Prohibition.Act..1961[data$District..Area == "Total District(s)"]
my_bar = barplot(DowryCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,3000), main=" Dowry Prohibition Act(1961) Cases in states" )
text(my_bar,DowryCaseS+150, paste( DowryCaseS, sep="") ,cex=0.5)

plot_ly(values = DowryCaseS ,labels = States ,type = 'pie', title = " Dowry Prohibition Act(1961) Cases in states" )

Some statistic of this feature

Minimun_temp=min(DowryCaseS)
q_1_temp=quantile(DowryCaseS)[2]
Median_temp=median(DowryCaseS)
Mean_temp=mean(DowryCaseS)
q_3_temp=quantile(DowryCaseS)[4]
Max_temp=max(DowryCaseS)
Var_temp=var(DowryCaseS)
Skewness_temp=skewness(DowryCaseS)
Kurtosis_temp=kurtosis(DowryCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_DowryCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_DowryCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum 0.000000e+00
## 2 1st Quartile 0.000000e+00
## 3       Median 6.000000e+00
## 4         Mean 2.748333e+02
## 5 3rd Quartile 4.700000e+01
## 6          Max 2.766000e+03
## 7     Varience 2.498868e+00
## 8     Skewness 8.319575e+00
## 9     Kurtosis 4.246521e+05

COMMENT:

  • No of Cases under Dowry Prohibition Act is High in Uttar Pradesh with 2766 Cases. 28 percentage of Cases is happend in Uttar Pradesh, 2nd highest cases in Bihar(18.9 % of Cases) and 3rd highest cases in Jharkhand(14.2 % of Cases)
  • Arunachal Pradesh, Goa, Himacahl Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, Sikkim, Tripura, A & N Islands, Chandigarh, D&N Haveli, Daman & Diu and Lakshadweep has lowest cases (0 no of cases).

Column of Cases of Indecent Representation of Women Act (1986)

Visualize the Indecent Representation of Women Act (1986)

IndecentCaseS = data$Indecent.Representation.of.Women..P..Act..1986[data$District..Area == "Total District(s)"]
my_bar = barplot(IndecentCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,15), main= " Indecent Representation of Women Act(1986) Cases in states" )
text(my_bar,IndecentCaseS+0.8, paste(IndecentCaseS, sep="") ,cex=0.6)

plot_ly(values = IndecentCaseS ,labels = States ,type = 'pie', title =  " Indecent Representation of Women Act(1986) Cases in states")

Some statistic of this feature

Minimun_temp=min(IndecentCaseS)
q_1_temp=quantile(IndecentCaseS)[2]
Median_temp=median(IndecentCaseS)
Mean_temp=mean(IndecentCaseS)
q_3_temp=quantile(IndecentCaseS)[4]
Max_temp=max(IndecentCaseS)
Var_temp=var(IndecentCaseS)
Skewness_temp=skewness(IndecentCaseS)
Kurtosis_temp=kurtosis(IndecentCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_IndecentCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_IndecentCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic     Value
## 1      Minimum  0.000000
## 2 1st Quartile  0.000000
## 3       Median  0.000000
## 4         Mean  1.111111
## 5 3rd Quartile  1.000000
## 6          Max 12.000000
## 7     Varience  2.868491
## 8     Skewness 10.444092
## 9     Kurtosis  7.358730

COMMENT:

  • No of Indecent Representation of Women Act (1986) is High in Andhra Pradesh with 12 Cases. 30 percentage of Cases is happend in Andhra Pradesh, 2nd highest cases in Rajasthan(9 Cases) and 3rd highest cases in Tamil Nadu(7 of Cases)
  • We can that there are lot of States and Ut that has 0 Cases

Column of Protection of Women from Domestic Violence Act (2005)

Visualize the Protection of Women from Domestic Violence Act (2005)

DomesticViolenceCaseS = data$Protection.of.Women.from.Domestic.Violence.Act..2005[data$District..Area == "Total District(s)"]
my_bar = barplot(DomesticViolenceCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,200), main = "Cases under Protection of Women from Domestic Violence Act(2005) in states")
text(my_bar,DomesticViolenceCaseS+10, paste( DomesticViolenceCaseS, sep="") ,cex=0.6)

plot_ly(values = DomesticViolenceCaseS ,labels = States ,type = 'pie', title =  "Cases under Protection of Women from Domestic Violence Act(2005) in states")

Some statistic of this feature

Minimun_temp=min(DomesticViolenceCaseS)
q_1_temp=quantile(DomesticViolenceCaseS)[2]
Median_temp=median(DomesticViolenceCaseS)
Mean_temp=mean(DomesticViolenceCaseS)
q_3_temp=quantile(DomesticViolenceCaseS)[4]
Max_temp=max(DomesticViolenceCaseS)
Var_temp=var(DomesticViolenceCaseS)
Skewness_temp=skewness(DomesticViolenceCaseS)
Kurtosis_temp=kurtosis(DomesticViolenceCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_DomesticViolenceCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_DomesticViolenceCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic       Value
## 1      Minimum    0.000000
## 2 1st Quartile    0.000000
## 3       Median    0.000000
## 4         Mean   12.805556
## 5 3rd Quartile    4.000000
## 6          Max  161.000000
## 7     Varience    3.223636
## 8     Skewness   12.057937
## 9     Kurtosis 1326.675397

COMMENT:

  • No of Cases under Protection of Women from Domestic Violence Act(2005) in states is High in Bihar with 161 Cases. 34.9 percentage of Cases is happend in Bihar, 2nd highest cases in Kerala(28.6 % of Cases) and 3rd highest cases in Madhya Pradesh(19.7 % of Cases)
  • We can that there are lot of States and Ut that has 0 Cases

Column of Immoral Traffic Prevention Act Cases

Visualize the Immoral Traffic Prevention Act Cases

ImmoralCaseS = data$Immoral.Traffic.Prevention.Act[data$District..Area == "Total District(s)"]
my_bar = barplot(ImmoralCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,600), main= "Cases under Immoral Traffic Prevention Act in states" )
text(my_bar,ImmoralCaseS+20, paste( ImmoralCaseS, sep="") ,cex=0.6)

plot_ly(values = ImmoralCaseS ,labels = States ,type = 'pie', title =  "Cases under Immoral Traffic Prevention Act in states")

Some statistic of this feature

Minimun_temp=min(ImmoralCaseS)
q_1_temp=quantile(ImmoralCaseS)[2]
Median_temp=median(ImmoralCaseS)
Mean_temp=mean(ImmoralCaseS)
q_3_temp=quantile(ImmoralCaseS)[4]
Max_temp=max(ImmoralCaseS)
Var_temp=var(ImmoralCaseS)
Skewness_temp=skewness(ImmoralCaseS)
Kurtosis_temp=kurtosis(ImmoralCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_ImmoralCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_ImmoralCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum     0.000000
## 2 1st Quartile     0.000000
## 3       Median     5.000000
## 4         Mean    67.333333
## 5 3rd Quartile    52.500000
## 6          Max   491.000000
## 7     Varience     2.240404
## 8     Skewness     6.945632
## 9     Kurtosis 15837.142857

COMMENT:

  • No of Cases under Immoral Traffic Prevention Act in states is High in Tamil Nadu with 491 Cases. 20.3 percentage of Cases is happend in Tamil Nadu, 2nd highest cases in Karnataka(17.3 % of Cases) and 3rd highest cases in Maharashtra(15.7 % of Cases)
  • Jammu & Kashmir, Manipur, Meghalaya, Mizoram, Sikkim, A & N Islands, Chandigarh, D&N Haveli, Daman & Diu and Lakshadweep has lowest cases (0 no of cases).

Column of Total Crime Cases Against Women

Visualize of Total Crime Cases Against Women

TotalCrimesCaseS = data$Total.Crimes.against.Women[data$District..Area == "Total District(s)"]
my_bar = barplot(TotalCrimesCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,40000), main = "Total Crimes against Women in states" )
text(my_bar,TotalCrimesCaseS+1000, paste( TotalCrimesCaseS, sep="") ,cex=0.6)

plot_ly(values = TotalCrimesCaseS ,labels = States ,type = 'pie', title = "Total Crimes against Women in states" )

Some statistic of this feature

Minimun_temp=min(TotalCrimesCaseS)
q_1_temp=quantile(TotalCrimesCaseS)[2]
Median_temp=median(TotalCrimesCaseS)
Mean_temp=mean(TotalCrimesCaseS)
q_3_temp=quantile(TotalCrimesCaseS)[4]
Max_temp=max(TotalCrimesCaseS)
Var_temp=var(TotalCrimesCaseS)
Skewness_temp=skewness(TotalCrimesCaseS)
Kurtosis_temp=kurtosis(TotalCrimesCaseS)

# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_TotalCrimesCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_TotalCrimesCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
##      Statistic        Value
## 1      Minimum 9.000000e+00
## 2 1st Quartile 3.170000e+02
## 3       Median 5.505500e+03
## 4         Mean 9.094278e+03
## 5 3rd Quartile 1.533400e+04
## 6          Max 3.552700e+04
## 7     Varience 1.079722e+00
## 8     Skewness 2.989564e+00
## 9     Kurtosis 1.170274e+08

COMMENT:

  • Top States & UT with Highest Crimes Against Women
    • Uttar Pradesh has Highest crimes(35,527) on women with 10.9% of total cases
    • West Bengal has 2nd Highest crimes(33,218) on women with 10.1% of total cases
    • Maharashtra has 3rd Highest crimes(31,126) on women with 9.51% of total cases
    • Rajasthan has 4th Highest crimes(28,165) on women with 8.6% of total cases
  • Top States & UT with lowest Crimes Against Women
    • Lakshadweep has total 9 cases on women which is first lowest states
    • D&N Haveli has total 25 cases on women which is second lowest states
    • Daman & Diu has total 28 cases on women which is third lowest states
    • Sikkim has total 53 cases on women which is forth lowest states

CONTINGENCY TABLES OF DATA

Contingency table of Rape Cases in States

Categorizing the Rape Case columns in 6 categories:

s=data2$Rape

n=length(s)

y_dummy=c()

# categorization in 5 part
b = (max(s)-min(s))/6
a = c(min(s)-1,15, 45, 75,105, 150, 6*b)

for( i in 1:n){
  
  for (j in 1:(length(a)-1))
    {
    if(a[j]<s[i] & s[i]<=a[j+1])
      y_dummy[i]=j
  
    }
}

summary(factor(y_dummy))
##   1   2   3   4   5   6 
## 281 263 134  64  45  29

COMMENT:

Dividing RapeCase columns in 6 Categaries

Categories Interval(Cases)
1 0 - 15
2 15 - 45
3 45 - 75
4 75 -105
5 105- 150
6 150 - 712

Creating the function for Categorizing the Rape Case columns in 6 categories!

dummy_x = function(x){
      s=x
    
      n=length(s)
    
      x_dummy=c()
    
      # categorization in 5 part
      b = (max(s)-min(s))/6
      a = c(min(s)-1,15, 45, 75,105, 150, max(s))
    
      for( i in 1:n){
      
        for (j in 1:(length(a)-1))
          {
          if(a[j]<s[i] & s[i]<=a[j+1])
            x_dummy[i]=j
            x_dummy
      
          }
      }
    
      x_dummy
}

COMMENT:

Dividing RapeCase columns in 6 Categaries

Categories Interval(Cases)
1 0 - 15
2 15 - 45
3 45 - 75
4 75 -105
5 105- 150
6 150 - max(columns)

Creating function for contingency table 

conti = function(x){
  
  y = y_dummy
  O_ij = table(y, x)
  
  u_i = rowSums(O_ij)
  v_j = colSums(O_ij)
  
  E_ij = outer(u_i, v_j)/sum(O_ij)
  
  z=sum(((O_ij - E_ij)^2)/E_ij)
  
  message("Measure of Association = ",z)
  print(O_ij)
  print(E_ij)
  
  # To check any measure of associtiom 
  df = (dim(O_ij)[1]-1)*(dim(O_ij)[2] - 1); df
  p =1- pchisq(z, df )
  message("there P value is = ",p)
  if(p >= 0.05){
    print("There is no Association, Which mean x and y are Independent.")
  }
  else{
    print("There is Association, Which mean X and Y are Dependent.")
  }

}

Column Attempt to commit Rape cases vs Rape cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Attempt.to.commit.Rape))
## Measure of Association = 109.609070012231
##    x
## y     1   2   3   4   5   6
##   1 278   3   0   0   0   0
##   2 239  23   1   0   0   0
##   3 114  13   2   2   2   1
##   4  54  10   0   0   0   0
##   5  40   3   0   0   1   1
##   6  23   1   3   1   0   1
##           1         2         3         4         5         6
## 1 257.58333 18.251225 2.0661765 1.0330882 1.0330882 1.0330882
## 2 241.08333 17.082108 1.9338235 0.9669118 0.9669118 0.9669118
## 3 122.83333  8.703431 0.9852941 0.4926471 0.4926471 0.4926471
## 4  58.66667  4.156863 0.4705882 0.2352941 0.2352941 0.2352941
## 5  41.25000  2.922794 0.3308824 0.1654412 0.1654412 0.1654412
## 6  26.58333  1.883578 0.2132353 0.1066176 0.1066176 0.1066176
## there P value is = 1.44084744135853e-12
## [1] "There is Association, Which mean X and Y are Dependent."

Column Kidnapping Cases vs Rape cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Kidnapping...Abduction_Total))
## Measure of Association = 487.925764143553
##    x
## y     1   2   3   4   5   6
##   1 180  78  17   5   1   0
##   2  37  81  49  37  35  24
##   3  11  25  36  24  10  28
##   4   5   6  12   6  18  17
##   5   1   3   9   8  10  14
##   6   0   0   2   1   6  20
##           1         2         3         4         5         6
## 1 80.580882 66.462010 43.045343 27.893382 27.549020 35.469363
## 2 75.419118 62.204657 40.287990 26.106618 25.784314 33.197304
## 3 38.426471 31.693627 20.526961 13.301471 13.137255 16.914216
## 4 18.352941 15.137255  9.803922  6.352941  6.274510  8.078431
## 5 12.904412 10.643382  6.893382  4.466912  4.411765  5.680147
## 6  8.316176  6.859069  4.442402  2.878676  2.843137  3.660539
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Dowry Deaths Cases Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Dowry.Deaths))
## Measure of Association = 102.19981241732
##    x
## y     1   2   3   4
##   1 274   7   0   0
##   2 183  70   9   1
##   3  96  32   3   3
##   4  47  13   3   1
##   5  30  13   2   0
##   6  16  12   1   0
##           1         2         3         4
## 1 222.45833 50.621324 6.1985294 1.7218137
## 2 208.20833 47.378676 5.8014706 1.6115196
## 3 106.08333 24.139706 2.9558824 0.8210784
## 4  50.66667 11.529412 1.4117647 0.3921569
## 5  35.62500  8.106618 0.9926471 0.2757353
## 6  22.95833  5.224265 0.6397059 0.1776961
## there P value is = 4.9960036108132e-15
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Assault on Women with intent to outrage her Modesty Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Assault.on.Women.with.intent.to.outrage.her.Modesty_Total))
## Measure of Association = 616.492832906999
##    x
## y     1   2   3   4   5   6
##   1 177  67  14   8   6   9
##   2  33  75  54  38  32  31
##   3   5  10  26  21  27  45
##   4   3   3   4   5  14  35
##   5   0   0   0   3   3  39
##   6   0   0   1   0   1  27
##           1         2         3         4         5         6
## 1 75.071078 53.376225 34.091912 25.827206 28.582108 64.051471
## 2 70.262255 49.957108 31.908088 24.172794 26.751225 59.948529
## 3 35.799020 25.453431 16.257353 12.316176 13.629902 30.544118
## 4 17.098039 12.156863  7.764706  5.882353  6.509804 14.588235
## 5 12.022059  8.547794  5.459559  4.136029  4.577206 10.257353
## 6  7.747549  5.508578  3.518382  2.665441  2.949755  6.610294
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Insult to the Modesty of Women Cases Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Insult.to.the.Modesty.of.Women_Total))
## Measure of Association = 208.618007731855
##    x
## y     1   2   3   4   5   6
##   1 275   6   0   0   0   0
##   2 249   6   2   2   1   3
##   3 112  17   1   1   2   1
##   4  44  10   4   1   4   1
##   5  31  10   1   0   2   1
##   6  11   7   1   2   3   5
##           1         2         3         4         5         6
## 1 248.62990 19.284314 3.0992647 2.0661765 4.1323529 3.7879902
## 2 232.70343 18.049020 2.9007353 1.9338235 3.8676471 3.5453431
## 3 118.56373  9.196078 1.4779412 0.9852941 1.9705882 1.8063725
## 4  56.62745  4.392157 0.7058824 0.4705882 0.9411765 0.8627451
## 5  39.81618  3.088235 0.4963235 0.3308824 0.6617647 0.6066176
## 6  25.65931  1.990196 0.3198529 0.2132353 0.4264706 0.3909314
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Cruelty by Husband or his Relatives Cases Vs Rape Case

Contingency table and conclusion of Association

conti(dummy_x(data2$Cruelty.by.Husband.or.his.Relatives))
## Measure of Association = 466.914867473824
##    x
## y     1   2   3   4   5   6
##   1 193  46  17  10   6   9
##   2  40  56  44  24  29  70
##   3   6  18  14  12  22  62
##   4   4   2   5   5  10  38
##   5   1   0   5   6   3  30
##   6   0   0   1   0   1  27
##           1         2         3         4         5         6
## 1 84.024510 42.012255 29.615196 19.628676 24.449755 81.269608
## 2 78.642157 39.321078 27.718137 18.371324 22.883578 76.063725
## 3 40.068627 20.034314 14.122549  9.360294 11.659314 38.754902
## 4 19.137255  9.568627  6.745098  4.470588  5.568627 18.509804
## 5 13.455882  6.727941  4.742647  3.143382  3.915441 13.014706
## 6  8.671569  4.335784  3.056373  2.025735  2.523284  8.387255
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Importation of Girls from Foreign Country Cases Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Importation.of.Girls.from.Foreign.Country))
## Measure of Association = 0
##    x
## y     1
##   1 281
##   2 263
##   3 134
##   4  64
##   5  45
##   6  29
##     1
## 1 281
## 2 263
## 3 134
## 4  64
## 5  45
## 6  29
## there P value is = 1
## [1] "There is no Association, Which mean x and y are Independent."

Column of Abetment of Suicides of Women Cases Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Abetment.of.Suicides.of.Women))
## Measure of Association = 134.050400384235
##    x
## y     1   2   3   4
##   1 278   3   0   0
##   2 246  15   2   0
##   3 116  17   1   0
##   4  52   9   3   0
##   5  30  11   3   1
##   6  18   9   0   2
##           1         2         3         4
## 1 254.82843 22.039216 3.0992647 1.0330882
## 2 238.50490 20.627451 2.9007353 0.9669118
## 3 121.51961 10.509804 1.4779412 0.4926471
## 4  58.03922  5.019608 0.7058824 0.2352941
## 5  40.80882  3.529412 0.4963235 0.1654412
## 6  26.29902  2.274510 0.3198529 0.1066176
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Cases under Dowry Prohibition Act (1961) Vs Rape Cases

Contigency table and conclusion of Association

conti(dummy_x(data2$Dowry.Prohibition.Act..1961))
## Measure of Association = 71.9454194969891
##    x
## y     1   2   3   4   5   6
##   1 253  22   5   0   1   0
##   2 195  24  21   6  10   7
##   3 120   4   2   2   2   4
##   4  59   0   2   0   0   3
##   5  42   1   0   0   0   2
##   6  28   1   0   0   0   0
##           1         2         3         4         5         6
## 1 240.02083 17.906863 10.330882 2.7549020 4.4767157 5.5098039
## 2 224.64583 16.759804  9.669118 2.5784314 4.1899510 5.1568627
## 3 114.45833  8.539216  4.926471 1.3137255 2.1348039 2.6274510
## 4  54.66667  4.078431  2.352941 0.6274510 1.0196078 1.2549020
## 5  38.43750  2.867647  1.654412 0.4411765 0.7169118 0.8823529
## 6  24.77083  1.848039  1.066176 0.2843137 0.4620098 0.5686275
## there P value is = 1.97045729766199e-06
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Cases of Indecent Representation of Women Act (1986) Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Indecent.Representation.of.Women..P..Act..1986))
## Measure of Association = 0
##    x
## y     1
##   1 281
##   2 263
##   3 134
##   4  64
##   5  45
##   6  29
##     1
## 1 281
## 2 263
## 3 134
## 4  64
## 5  45
## 6  29
## there P value is = 1
## [1] "There is no Association, Which mean x and y are Independent."

Column of Protection of Women from Domestic Violence Act (2005) Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Protection.of.Women.from.Domestic.Violence.Act..2005))
## Measure of Association = 21.8575126018189
##    x
## y     1   2   4   6
##   1 281   0   0   0
##   2 261   1   0   1
##   3 133   1   0   0
##   4  64   0   0   0
##   5  44   0   1   0
##   6  29   0   0   0
##           1          2          4          6
## 1 279.62255 0.68872549 0.34436275 0.34436275
## 2 261.71078 0.64460784 0.32230392 0.32230392
## 3 133.34314 0.32843137 0.16421569 0.16421569
## 4  63.68627 0.15686275 0.07843137 0.07843137
## 5  44.77941 0.11029412 0.05514706 0.05514706
## 6  28.85784 0.07107843 0.03553922 0.03553922
## there P value is = 0.111594884243551
## [1] "There is no Association, Which mean x and y are Independent."

Column of Immoral Traffic Prevention Act Cases Vs Rape Cases

Contingency table and conclusion of Association

conti(dummy_x(data2$Immoral.Traffic.Prevention.Act))
## Measure of Association = 78.3189716619971
##    x
## y     1   2   3   5   6
##   1 276   5   0   0   0
##   2 256   5   1   0   1
##   3 130   3   1   0   0
##   4  59   5   0   0   0
##   5  43   1   0   0   1
##   6  23   2   2   1   1
##           1         2         3          5         6
## 1 271.01348 7.2316176 1.3774510 0.34436275 1.0330882
## 2 253.65319 6.7683824 1.2892157 0.32230392 0.9669118
## 3 129.23775 3.4485294 0.6568627 0.16421569 0.4926471
## 4  61.72549 1.6470588 0.3137255 0.07843137 0.2352941
## 5  43.40074 1.1580882 0.2205882 0.05514706 0.1654412
## 6  27.96936 0.7463235 0.1421569 0.03553922 0.1066176
## there P value is = 7.55926055084899e-09
## [1] "There is Association, Which mean X and Y are Dependent."

Column of Total Crime Cases Against Women

Contingency table and conclusion of Association

conti(dummy_x(data2$Total.Crimes.against.Women))
## Measure of Association = 530.824724342884
##    x
## y     1   2   3   4   5   6
##   1 101  50  28  26  28  48
##   2   0   1   7  11  30 214
##   3   0   0   0   0   1 133
##   4   0   0   0   0   0  64
##   5   0   0   0   0   0  45
##   6   0   0   0   0   0  29
##           1       2         3         4         5         6
## 1 34.780637 17.5625 12.052696 12.741422 20.317402 183.54534
## 2 32.552696 16.4375 11.280637 11.925245 19.015931 171.78799
## 3 16.585784  8.3750  5.747549  6.075980  9.688725  87.52696
## 4  7.921569  4.0000  2.745098  2.901961  4.627451  41.80392
## 5  5.569853  2.8125  1.930147  2.040441  3.253676  29.39338
## 6  3.589461  1.8125  1.243873  1.314951  2.096814  18.94240
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."

COMMENT:

  • Column Which has Association with Rape Cases
    • Attempt to commit Rape
    • Kidnapping Abduction
    • Dowry Deaths
    • Assault on Women with intent to outrage her Modesty
    • Insult to the Modesty of Women
    • Cruelty by Husband or his Relatives
    • Abetment of Suicides of Women
    • Dowry Prohibition Act 1961
    • Immoral Traffic Prevention Act
    • Total Crimes against Women
  • Column Which has no Association with Rape Cases
    • Importation of girls from foreign country
    • Indecent Representation of Women Act (1986)
    • Protection of women from domestic Violence act (2005)
  • COMMENT:-
    • If we see above result It is shocking to see that Indecent Representation doesn’t effect the Rape Cases.
    • Most of the cases happning on women is due to relatives and Society which is also associate with Rape Cases.